






'''simple docstring'''



import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE							=						'src/transformers'
SCREAMING_SNAKE_CASE							=						'docs/source/en'
SCREAMING_SNAKE_CASE							=						'.'


def 				lowercase_					(     __A			:							List[Any]       ,   __A			:							Dict       ,   __A			:							int      )							->   List[str]:



 """simple docstring"""




 with open(__A       ,   '''r'''       ,   encoding='''utf-8'''       ,   newline='''\n'''      ) as f:
  lowercase       : Optional[Any]						=f.readlines()
 # Find the start prompt.
 lowercase       : Optional[int]						=0
 while not lines[start_index].startswith(__A      ):
  start_index += 1
 start_index += 1

 lowercase       : str						=start_index
 while not lines[end_index].startswith(__A      ):
  end_index += 1
 end_index -= 1

 while len(lines[start_index]      ) <= 1:
  start_index += 1
 while len(lines[end_index]      ) <= 1:
  end_index -= 1
 end_index += 1
 return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE							=						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE							=						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE							=						re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE							=						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE							=						direct_transformers_import(TRANSFORMERS_PATH)


def 				lowercase_					(     __A			:							Union[str, Any]      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase       : Optional[int]						=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,   __A      )
 return [m.group(0      ) for m in matches]


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							List[Any]      )							->   int:



 """simple docstring"""




 lowercase       : int						=2 if text == '''✅''' or text == '''❌''' else len(__A      )
 lowercase       : int						=(width - text_length) // 2
 lowercase       : Dict						=width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent


def 				lowercase_					(     )							->   int:



 """simple docstring"""




 lowercase       : List[str]						=transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase       : Union[str, Any]						={
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowercase       : Dict						={name: config.replace('''Config'''       ,   ''''''      ) for name, config in model_name_to_config.items()}

 # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
 lowercase       : Optional[int]						=collections.defaultdict(__A      )
 lowercase       : List[Any]						=collections.defaultdict(__A      )
 lowercase       : List[Any]						=collections.defaultdict(__A      )
 lowercase       : str						=collections.defaultdict(__A      )
 lowercase       : int						=collections.defaultdict(__A      )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(__A      ):
  lowercase       : int						=None
  if attr_name.endswith('''Tokenizer'''      ):
   lowercase       : Optional[Any]						=slow_tokenizers
   lowercase       : Dict						=attr_name[:-9]
  elif attr_name.endswith('''TokenizerFast'''      ):
   lowercase       : Optional[int]						=fast_tokenizers
   lowercase       : Optional[Any]						=attr_name[:-1_3]
  elif _re_tf_models.match(__A      ) is not None:
   lowercase       : Optional[Any]						=tf_models
   lowercase       : Any						=_re_tf_models.match(__A      ).groups()[0]
  elif _re_flax_models.match(__A      ) is not None:
   lowercase       : Any						=flax_models
   lowercase       : Optional[int]						=_re_flax_models.match(__A      ).groups()[0]
  elif _re_pt_models.match(__A      ) is not None:
   lowercase       : List[str]						=pt_models
   lowercase       : Dict						=_re_pt_models.match(__A      ).groups()[0]

  if lookup_dict is not None:
   while len(__A      ) > 0:
    if attr_name in model_name_to_prefix.values():
     lowercase       : Optional[int]						=True
     break
    # Try again after removing the last word in the name
    lowercase       : List[str]						=''''''.join(camel_case_split(__A      )[:-1]      )

    # Let's build that table!
 lowercase       : List[Any]						=list(model_name_to_config.keys()      )
 model_names.sort(key=str.lower      )
 lowercase       : Tuple						=['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
 # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
 lowercase       : Optional[Any]						=[len(__A      ) + 2 for c in columns]
 lowercase       : int						=max([len(__A      ) for name in model_names]      ) + 2

 # Build the table per se
 lowercase       : Any						='''|''' + '''|'''.join([_center_text(__A       ,   __A      ) for c, w in zip(__A       ,   __A      )]      ) + '''|\n'''
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]      ) + "|\n"

 lowercase       : Dict						={True: '''✅''', False: '''❌'''}
 for name in model_names:
  lowercase       : Dict						=model_name_to_prefix[name]
  lowercase       : Dict						=[
      name,
      check[slow_tokenizers[prefix]],
      check[fast_tokenizers[prefix]],
      check[pt_models[prefix]],
      check[tf_models[prefix]],
      check[flax_models[prefix]],
  ]
  table += "|" + "|".join([_center_text(__A       ,   __A      ) for l, w in zip(__A       ,   __A      )]      ) + "|\n"
 return table


def 				lowercase_					(     __A			:							Any=False      )							->   Optional[Any]:



 """simple docstring"""




 lowercase	,		lowercase	,		lowercase	,		lowercase       : Optional[Any]						=_find_text_in_file(
     filename=os.path.join(__A       ,   '''index.md'''      )       ,   start_prompt='''<!--This table is updated automatically from the auto modules'''       ,   end_prompt='''<!-- End table-->'''       ,   )
 lowercase       : List[Any]						=get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(__A       ,   '''index.md'''      )       ,   '''w'''       ,   encoding='''utf-8'''       ,   newline='''\n'''      ) as f:
    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
  else:
   raise ValueError(
       '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      check_model_table(args.fix_and_overwrite)











'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  snake_case__      (					UpperCamelCase):
					a_        =					["image_processor", "tokenizer"]
					a_        =					"LayoutLMv2ImageProcessor"
					a_        =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




					def __init__(  self  :		Optional[int]				, _A  :		str=None				, _A  :		Optional[Any]=None				, **_A  :		Any      )    ->							Tuple:
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				, _A				, )
											UpperCAmelCase_      :       int          =       kwargs.pop('''feature_extractor'''      )

								UpperCAmelCase_      :       str          =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''      )
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''      )

								super().__init__(_A				, _A      )




					def __call__(  self  :		str				, _A  :		Optional[int]				, _A  :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _A  :		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				, _A  :		Union[List[List[int]], List[List[List[int]]]] = None				, _A  :		Optional[Union[List[int], List[List[int]]]] = None				, _A  :		bool = True				, _A  :		Union[bool, str, PaddingStrategy] = False				, _A  :		Union[bool, str, TruncationStrategy] = None				, _A  :		Optional[int] = None				, _A  :		int = 0				, _A  :		Optional[int] = None				, _A  :		Optional[bool] = None				, _A  :		Optional[bool] = None				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = True				, _A  :		Optional[Union[str, TensorType]] = None				, **_A  :		Dict				, )    ->							BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes '''
											    '''if you initialized the image processor with apply_ocr set to True.'''      )

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''      )

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''      )

								# first, apply the image processor
								UpperCAmelCase_      :       int          =       self.image_processor(images=_A				, return_tensors=_A      )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       int          =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
											UpperCAmelCase_      :       int          =       features['''words''']

								UpperCAmelCase_      :       str          =       self.tokenizer(
								    text=text if text is not None else features['''words''']				, text_pair=text_pair if text_pair is not None else None				, boxes=boxes if boxes is not None else features['''boxes''']				, word_labels=_A				, add_special_tokens=_A				, padding=_A				, truncation=_A				, max_length=_A				, stride=_A				, pad_to_multiple_of=_A				, return_token_type_ids=_A				, return_attention_mask=_A				, return_overflowing_tokens=_A				, return_special_tokens_mask=_A				, return_offsets_mapping=_A				, return_length=_A				, verbose=_A				, return_tensors=_A				, **_A				, )

								# add pixel values
								UpperCAmelCase_      :       int          =       features.pop('''pixel_values'''      )
								if return_overflowing_tokens is True:
											UpperCAmelCase_      :       List[Any]          =       self.get_overflowing_images(_A				, encoded_inputs['''overflow_to_sample_mapping''']      )
								UpperCAmelCase_      :       Optional[int]          =       images

								return encoded_inputs




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Tuple      )    ->							Dict:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								UpperCAmelCase_      :       Tuple          =       []
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]      )

								if len(_A      ) != len(_A      ):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F" {len(_A      )} and {len(_A      )}"      )

								return images_with_overflow




					def 		A      (  self  :		Optional[Any]				, *_A  :		Union[str, Any]				, **_A  :		Union[str, Any]      )    ->							Tuple:
								return self.tokenizer.batch_decode(*_A				, **_A      )




					def 		A      (  self  :		Any				, *_A  :		Optional[Any]				, **_A  :		Tuple      )    ->							Tuple:
								return self.tokenizer.decode(*_A				, **_A      )




					@property
					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								return ["input_ids", "bbox", "attention_mask", "image"]




					@property
					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				, _A				, )
								return self.image_processor_class





					@property
					def 		A      (  self  :		Tuple      )    ->							str:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				, _A				, )
								return self.image_processor


